Session Category: Customer

October 25, 2018

9:30  -  10:30

Grand Cypress H

105: Using Machine Learning Algorithm to Personalize Customer Experience

SMUD recently revamped its strategy to increase efficiency while maintaining, or improving the quality of life for customers who need help the most. As one of tactics to achieve this goal, SMUD wants to offer these customers customized programs to reduce energy usage and increase their feeling of control. We built an energy specialist application … Continued

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305: Promoting a Culture of Evidence: the Use of Customer Data and Predictive Analytics in Southern California Edison’s Low Income Programs

1:45  -  2:45

Grand Cypress H

305: Promoting a Culture of Evidence: the Use of Customer Data and Predictive Analytics in Southern California Edison’s Low Income Programs

This session will present on the different applications of predictive analytics and other multivariate statistical techniques in formulating more evidence-based programs and policies within Southern California Edison’s (SCE) Income Qualified Programs (IQP)/low income programs, in particular the California Alternate Rates for Energy (CARE) and the Family Electric Rate Assistance (FERA) programs. More specifically, the session … Continued

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405: High Usage Alert Modeling

4:00  -  5:00

Grand Cypress H

405: High Usage Alert Modeling

This presentation will outline Entergy’s initiative to develop a series of models that predict (using monthly data) when a customer will largely exceed their normal usage to enable the customer experience group to engage proactively and help alleviate call volumes. We will cover our initial engagement with the business in order to create the use … Continued

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October 26, 2018

8:30  -  9:30

Grand Cypress H

505: Understanding Customers’ Temperature Sensitivity Using Segmented Regressions

People have different sensitivity over home temperature. Understanding the response of the customer’s electricity usage to external temperature could help utilities market groups of customers differently. Here we present a study where we fitted segmented linear regression models for over 500,000 residential customers in our service territory using the electricity usage and temperature data. The … Continued

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